
> ## ---- Setup ----
> ## lmer estimation algorithm control
> strict_tol <- lmerControl(optCtrl = list(xtol_abs = 1e-8,
+                                          ftol_abs = 1e-8))

> ## ---- Data ----
> ## Inflation adjustment factors
> infladj <- readr::read_csv("dat/aux/inflation_adjustment.csv") %>%
+   dplyr::filter(year %in% 2014:2018) %>%
+   dplyr::mutate(inflation = cpi / cpi[year == 2014]) %>%
+   dplyr::mutate(adjustment = 1 / inflation)
[1mindexed[0m [32m0B[0m in [36m 0s[0m, [32m0B/s[0m[1mindexed[0m [32m1.00TB[0m in [36m 0s[0m, [32m731.10TB/s[0m                                                                                                                                                              
> ## ---- Data ----
> ## SOEP data
> load("dat/proc-data/soep_imp.RData")

> ## ---- Shortened TSCS: AfD analyses, 2014-2018 (demeaning) ----
> ## Variable selection for demeaning
> current_dm_vars <- soep_imp$imputations$imp1 %>%
+   dplyr::select(dplyr::ends_with("_cwu")) %>%
+   names() %>%
+   substr(., 1, nchar(.) - 4)

> additional_dm_vars <- c(
+   "nbh_met_p_deutschl",
+   "nbh_alq_p_quote",
+   "kr_uemprate",
+   "kr_foreigner"
+ )

> dm_vars <- c(current_dm_vars, additional_dm_vars)

> ## Updated regional data
> regional_data <- rio::import(
+   "../../soep-data/soep.v38/stata/regionl.dta"
+ ) %>%
+   dplyr::filter(kkz > 0) %>%
+   dplyr::select(kkz,
+                 syear,
+                 kr_uemprate,
+                 kr_foreigner) %>%
+   dplyr::rename(year = syear) %>%
+   dplyr::mutate_at(
+     .vars = vars(kr_uemprate,
+                  kr_foreigner),
+     .funs = ~ifelse(. < 0, NA_real_, .)
+   ) %>%
+   dplyr::distinct() %>%
+   dplyr::group_by(kkz, year) %>% 
+   dplyr::summarize_all(mean) %>%
+   dplyr::ungroup()

> ## Data management
> for (m in seq_along(soep_imp$imputations)) {
+   soep_imp$imputations[[m]] <- soep_imp$imputations[[m]] %>%
+     dplyr::mutate(yrs_at_current_address = year - hh_at_current_address,
+                   age_at_current_address = age - yrs_at_current_address) %>%
+     dplyr::mutate(ltr_since_3 = as.numeric(yrs_at_current_address >= 3),
+                   ltr_since_5 = as.numeric(yrs_at_current_address >= 5),
+                   ltr_since_7 = as.numeric(yrs_at_current_address >= 7),
+                   ltr_age_30 = as.numeric(age_at_current_address <= 30)) %>%
+     # filter(social_housing == 0 | is.na(social_housing)) %>%
+     dplyr::mutate(lw = as.numeric(lef == 1 | spd == 1 | gre == 1),
+                   rw = as.numeric(cdu == 1 | fdp == 1 | afd == 1)) %>%
+     filter(renter_no_rent == 0) %>%
+     dplyr::select(
+       id,
+       hh_id,
+       plz,
+       kkz,
+       year,
+       year_fac,
+       weight,
+       starts_with("cold_rent_load"),
+       starts_with("cold_rent_sqm"),
+       wr_ecego,
+       wr_immig,
+       afd,
+       vote_afd,
+       gre,
+       lef,
+       fdp,
+       cdu,
+       spd,
+       non,
+       lw,
+       rw,
+       owner,
+       all_of(dm_vars),
+       starts_with("rent"),
+       starts_with("cmr_arm"),
+       starts_with("log_hinc_eq"),
+       starts_with("gtyp"),
+       starts_with("prop_personal_hinc"),
+       starts_with("hh_prop_ecact"),
+       starts_with("hh_mmb"),
+       starts_with("mover"),
+       starts_with("lm_part"),
+       starts_with("edu"),
+       starts_with("age"),
+       starts_with("east"),
+       starts_with("fem"),
+       starts_with("east"),
+       starts_with("ltr_"),
+       social_housing
+     ) %>%
+     dplyr::filter(not(is.na(rent_umn))) %>%
+     dplyr::filter(not(is.na(rent_cwu))) %>%
+     dplyr::filter(not(is.na(cmr_arm_umn))) %>%
+     dplyr::filter(not(is.na(cmr_arm_cwu)))
+ }

> ## Local market data
> load("dat/proc-data/plz5_f_und_b.RData")

> plz5_f_und_b <- plz5_f_und_b %>%
+   st_set_geometry(NULL) %>%
+   filter(year %in% 2005:2018) %>%
+   dplyr::select(plz, year, cmr_arm, rent) %>%
+   distinct() %>%
+   group_by(plz) %>%
+   mutate(
+     cmr_arm_2005 = mean(cmr_arm[year %in% 2005:2007], na.rm = TRUE),
+     cmr_arm_2018 = mean(cmr_arm[year %in% 2016:2018], na.rm = TRUE),
+     cmr_arm_chg = cmr_arm_2018 - cmr_arm_2005
+   ) %>%
+   mutate(
+     rent_2005 = mean(rent[year %in% 2005:2007], na.rm = TRUE),
+     rent_2018 = mean(rent[year %in% 2016:2018], na.rm = TRUE),
+     rent_chg = rent_2018 - rent_2005,
+     cmr_arm_chg = ifelse(is.na(cmr_arm_chg), rent_chg, cmr_arm_chg),
+     cmr_arm = ifelse(is.na(cmr_arm), rent, cmr_arm)
+   ) %>%
+   ungroup() %>%
+   group_by(year) %>%
+   mutate(
+     rent_cat =
+       ifelse(
+         year == 2004,
+         NA_integer_,
+         cut(
+           rent,
+           breaks = quantile(rent, c(0, 1 / 3, 2 / 3, 1), na.rm = TRUE),
+           labels = paste0("T", 1:3)
+         )
+       ),
+     cmr_arm_cat =
+       cut(
+         cmr_arm,
+         breaks = quantile(cmr_arm, c(0, 1 / 3, 2 / 3, 1), na.rm = TRUE),
+         labels = paste0("T", 1:3)
+       )
+   ) %>% 
+   ungroup() %>%
+   dplyr::select(plz, cmr_arm_chg, rent_chg, rent_cat, cmr_arm_cat, year) %>%
+   distinct() %>%
+   na.omit() %>%
+   mutate(
+     cmr_arm_chg_cat =
+       cut(
+         cmr_arm_chg,
+         breaks = quantile(cmr_arm_chg, c(0, 1 / 3, 2 / 3, 1)),
+         labels = paste0("T", 1:3)
+       ),
+     rent_chg_cat =
+       cut(
+         rent_chg,
+         breaks = quantile(rent_chg, c(0, 1 / 3, 2 / 3, 1)),
+         labels = paste0("T", 1:3)
+       )
+   )

> ## Additional data management
> for (m in seq_along(soep_imp$imputations)) {
+   soep_imp$imputations[[m]] <- soep_imp$imputations[[m]] %>%
+     # top code household rents per sqm
+     mutate(
+       cold_rent_sqm = case_when(
+         cold_rent_sqm > 40 ~ 40,
+         TRUE ~ cold_rent_sqm
+       )
+     ) %>% 
+     group_by(year) %>%
+     mutate(
+       log_hinc_eq_cat =
+         cut(
+           log_hinc_eq,
+           breaks = quantile(log_hinc_eq, c(0, 1 / 3, 2 / 3, 1)),
+           labels = paste0("T", 1:3)
+         )
+     ) %>%
+     ungroup() %>%
+     mutate(
+       gtyp4 = case_when(
+         gtyp %in% c(
+           "[1] Kernstaedte>==gr.Verdraum",
+           "[2] Kernstaedte<==gr.Verdraum",
+           "[9] Kernstaedte<==Verdansatz"
+         ) ~ "urban",
+         gtyp %in% c(
+           "[7] Mi-zent.=laendl=gr.Verdraum",
+           "[10] Mi-zent.=verd.=Verdansatz",
+           "[12] Mi-zent.=laendl=Verdansatz",
+           "[14] Mi-zent.=verd.=laendl.",
+           "[16] Mi-zent.=laendl=laendl."
+         ) ~ "towns",
+         gtyp %in% c(
+           "[3] Mi-zent.=hochverd.=gr.Verdraum",
+           "[5] Mi-zent.=verd.=gr.Verdraum",
+           "[4] so.Gem.=hochverd.=gr.Verdraum",
+           "[6] so.Gem.=verd.=gr.Verdraum"
+         ) ~ "suburban",
+         gtyp %in% c(
+           "[8] so.Gem.=laendl=gr.Verdraum",
+           "[11] so.Gem.=verd.=Verdansatz",
+           "[13] so.Gem.=laendl=Verdansatz",
+           "[15] so.Gem.=verd.=laendl.",
+           "[17] so.Gem.=laendl=laendl."
+         ) ~ "rural",
+         TRUE ~ NA_character_
+       ),
+       gtyp3 = ifelse(gtyp4 == "towns", "rural", gtyp4)
+     ) %>%
+     left_join(plz5_f_und_b,
+               by = c("plz", "year")) %>%
+     dplyr::filter(weight > 0)
+   
+   ## Repair names
+   names(soep_imp$imputations[[m]]) <- 
+     gsub(" ", "_", names(soep_imp$imputations[[m]]))
+ }

> ## Join county-level data, within-demeaning
> for (m in seq_along(soep_imp$imputations)) {
+   soep_imp$imputations[[m]] <- soep_imp$imputations[[m]] %>%
+     dplyr::select(-dplyr::starts_with("kr_")) %>%
+     dplyr::left_join(regional_data,
+                      by = c("kkz", "year")) %>%
+     dplyr::select(-ends_with("_cwu"), -ends_with("_cwu")) 
+ }

> ## Apply inflation adjustment to all relevant variables
> soep_afd <- soep_imp

> soep_afd$imputations <-
+   lapply(soep_afd$imputations, function (imp) {
+     imp %>%
+       dplyr::filter(year >= 2014) %>%
+       dplyr::select(-dplyr::ends_with("_cwu")) %>%
+       dplyr::select(-dplyr::ends_with("_umn")) %>%
+       dplyr::left_join(infladj %>%
+                          dplyr::select(year, adjustment),
+                        by = "year") %>%
+       dplyr::mutate(
+         cold_rent_sqm = cold_rent_sqm * adjustment,
+         log_hinc_eq = log((exp(log_hinc_eq) - 1) * adjustment + 1),
+         cmr_arm = cmr_arm * adjustment
+       )
+   })

> ## 2014:2018 Data for AfD support analyses
> dm_vars <- gsub(" ", "_", dm_vars)

> soep_afd$imputations <-
+   lapply(soep_afd$imputations, function (imp) {
+     imp %>%
+       group_by(id) %>%
+       mutate_at(.vars = vars(all_of(dm_vars)),
+                 .funs = list(
+                   umn = ~ mean(., na.rm = T),
+                   cwu = ~ . - mean(., na.rm = T)
+                 )) %>%
+       ungroup()
+   })

> ## [2014, 2018] Data for vote choice analyses
> soep_vot <- soep_imp

> soep_vot$imputations <-
+   lapply(soep_afd$imputations, function (imp) {
+     imp %>%
+       dplyr::filter(year %in% c(2014, 2018)) %>%
+       dplyr::select(-dplyr::ends_with("_cwu")) %>%
+       dplyr::select(-dplyr::ends_with("_umn")) %>%
+       group_by(id) %>%
+       mutate_at(.vars = vars(all_of(dm_vars)),
+                 .funs = list(
+                   umn = ~ mean(., na.rm = T),
+                   cwu = ~ . - mean(., na.rm = T)
+                 )) %>%
+       ungroup()
+   })

> ## ---- Models ----
> ## Outcomes
> outcomes <- c(
+   "afd",
+   "vote_afd"
+ )

> ## Main predictors
> predictors <- predictors_aux <- c("cmr_arm")

> ## Renter controls
> rent_vars <- rent_vars_aux <- c(NA_character_,
+                                 "cold_rent_sqm")

> ## Moderators
> individual_moderators <-
+   individual_moderators_aux <- c(NA_character_,
+                                  "log_hinc_eq",
+                                  "log_hinc_eq_cat")

> regional_moderators <- c(
+   NA_character_,
+   "gtyp3",
+   "[_pred_]_cat",
+   "[_pred_]_chg_cat"
+ )

> regional_moderators_aux <- c(
+   NA_character_,
+   "gtyp3"
+ )

> ## Subsets
> subset_owner <- subset_owner_aux <- c("owner == 0",
+                                       "owner == 1")

> subset_ltr <- c(NA_character_,
+                 "ltr_since_3 == 0",
+                 "ltr_since_5 == 0",
+                 "ltr_since_3 == 1",
+                 "ltr_since_5 == 1")

> subset_ltr_aux <- "ltr_since_5 == 1"

> ## Model formula components
> predictor_terms <- predictor_terms_aux <- 
+   c("[_pred_]_cwu", "[_pred_]_umn")

> renter_control_terms <- renter_control_terms_aux <- 
+   c("[_rent_var_]_cwu", "[_rent_var_]_umn")

> common_control_terms <-
+   list(
+     "none_year_fe" = "year_fac",
+     "full" = c(
+       "log_hinc_eq_cwu",
+       "log_hinc_eq_umn",
+       "prop_personal_hinc_cwu",
+       "prop_personal_hinc_umn",
+       "hh_prop_ecact_cwu",
+       "hh_prop_ecact_umn",
+       "hh_mmb_cwu",
+       "hh_mmb_umn",
+       "lm_part_Atypical_umn",
+       "lm_part_Inactive_umn",
+       "lm_part_Unemployed_umn",
+       "lm_part_In_Education_umn",
+       "lm_part_Pensioners_umn",
+       "lm_part_Atypical_cwu",
+       "lm_part_Inactive_cwu",
+       "lm_part_Unemployed_cwu",
+       "lm_part_In_Education_cwu",
+       "lm_part_Pensioners_cwu",
+       "age",
+       "east",
+       "fem",
+       "edu5",
+       "gtyp3",
+       "year_fac"
+     )
+     ,
+     "full_plus2" = c(
+       "log_hinc_eq_cwu",
+       "log_hinc_eq_umn",
+       "prop_personal_hinc_cwu",
+       "prop_personal_hinc_umn",
+       "hh_prop_ecact_cwu",
+       "hh_prop_ecact_umn",
+       "hh_mmb_cwu",
+       "hh_mmb_umn",
+       "lm_part_Atypical_umn",
+       "lm_part_Inactive_umn",
+       "lm_part_Unemployed_umn",
+       "lm_part_In_Education_umn",
+       "lm_part_Pensioners_umn",
+       "lm_part_Atypical_cwu",
+       "lm_part_Inactive_cwu",
+       "lm_part_Unemployed_cwu",
+       "lm_part_In_Education_cwu",
+       "lm_part_Pensioners_cwu",
+       "age",
+       "east",
+       "fem",
+       "edu5",
+       "gtyp3",
+       "year_fac",
+       "nbh_met_p_deutschl_cwu",
+       "nbh_alq_p_quote_cwu",
+       "nbh_met_p_deutschl_umn",
+       "nbh_alq_p_quote_umn"
+     )
+   )

> common_control_terms_aux <- common_control_terms["full"]

> ## ---- Compile model code ----
> model_formula <-
+   'mod <- lmer(
+   [_yvar_] ~
+   [_xvars_](1 | id) + (1 | plz) + (1 | kkz),
+   data = [_data_],
+   subset = [_subset_],
+   control = strict_tol,
+   weights = weight
+ )'

> model_formula_list_main <- list()

> counter <- 0L

> for (outcome in outcomes) {
+   if (outcome %in% c("cold_rent_load",
+                      "cold_rent_sqm")) {
+     subset_owner_local <- subset_owner[1]
+   } else {
+     subset_owner_local <- subset_owner
+   }
+   for (predictor in predictors) {
+     for (moderator in individual_moderators) {
+       for (regional in regional_moderators) {
+         for (owner in subset_owner_local) {
+           if (owner == "owner == 1" |
+               outcome %in% c("cold_rent_load",
+                              "cold_rent_sqm")) {
+             rent_vars_local <- NA_character_
+           } else {
+             rent_vars_local <- rent_vars
+           }
+           
+           for (ltr in subset_ltr) {
+             for (rent_var in rent_vars_local) {
+               for (controls in names(common_control_terms)) {
+                 ## Counter
+                 counter <- counter + 1L
+                 
+                 ## Covariates
+                 if (is.na(moderator) & is.na(regional)) {
+                   xvars <-
+                     collapse_plus(predictor_terms) %>%
+                     gsub("\\[\\_pred\\_\\]",
+                          predictor,
+                          .)
+                   
+                   if (not(controls == "none")) {
+                     xvars <- paste0(xvars,
+                                     collapse_plus(common_control_terms[[controls]]))
+                   }
+                   
+                   if (not(is.na(rent_var))) {
+                     renter_controls_local <-
+                       gsub(
+                         "\\[\\_rent\\_var\\_\\]",
+                         rent_var,
+                         collapse_plus(renter_control_terms)
+                       )
+                     xvars <- paste0(xvars, renter_controls_local)
+                   }
+                 } else if (not(is.na(moderator)) & is.na(regional)) {
+                   xvars <-
+                     sapply(predictor_terms, collapse_by, moderator) %>%
+                     collapse_plus() %>%
+                     gsub("\\[\\_pred\\_\\]",
+                          predictor,
+                          .)
+                   
+                   if (not(controls == "none")) {
+                     xvars <- paste0(xvars,
+                                     collapse_plus(common_control_terms[[controls]]))
+                   }
+                   
+                   if (not(is.na(rent_var))) {
+                     renter_controls_local <-
+                       sapply(renter_control_terms,
+                              collapse_by,
+                              moderator) %>%
+                       collapse_plus() %>%
+                       gsub("\\[\\_rent\\_var\\_\\]",
+                            rent_var,
+                            .)
+                     xvars <- paste0(xvars, renter_controls_local)
+                   }
+                 } else if (is.na(moderator) & not(is.na(regional))) {
+                   xvars <-
+                     sapply(predictor_terms, collapse_by, regional) %>%
+                     collapse_plus() %>%
+                     gsub("\\[\\_pred\\_\\]",
+                          predictor,
+                          .)
+                   
+                   if (not(controls == "none")) {
+                     xvars <- paste0(xvars,
+                                     collapse_plus(common_control_terms[[controls]]))
+                   }
+                   
+                   if (not(is.na(rent_var))) {
+                     renter_controls_local <-
+                       sapply(renter_control_terms,
+                              collapse_by,
+                              regional) %>%
+                       collapse_plus() %>%
+                       gsub("\\[\\_rent\\_var\\_\\]",
+                            rent_var,
+                            .) %>%
+                       gsub("\\[\\_pred\\_\\]",
+                            predictor,
+                            .)
+                     xvars <- paste0(xvars, renter_controls_local)
+                   }
+                 } else if (not(is.na(moderator)) &
+                            not(is.na(regional))) {
+                   xvars <-
+                     sapply(predictor_terms,
+                            collapse_by,
+                            collapse_by(moderator, regional)) %>%
+                     collapse_plus() %>%
+                     gsub("\\[\\_pred\\_\\]",
+                          predictor,
+                          .)
+                   
+                   if (not(controls == "none")) {
+                     xvars <- paste0(xvars,
+                                     collapse_plus(common_control_terms[[controls]]))
+                   }
+                   
+                   if (not(is.na(rent_var))) {
+                     renter_controls_local <-
+                       sapply(
+                         renter_control_terms,
+                         collapse_by,
+                         collapse_by(moderator, regional)
+                       ) %>%
+                       collapse_plus() %>%
+                       gsub("\\[\\_rent\\_var\\_\\]",
+                            rent_var,
+                            .) %>%
+                       gsub("\\[\\_pred\\_\\]",
+                            predictor,
+                            .)
+                     
+                     xvars <- paste0(xvars, renter_controls_local)
+                   }
+                 }
+                 
+                 ## Data
+                 if (outcome == "afd") {
+                   data <- "soep_afd$imputations[[m]]"
+                   
+                 } else if (outcome == "vote_afd") {
+                   data <- "soep_vot$imputations[[m]]"
+                 } else {
+                   data <- "soep_imp$imputations[[m]]"
+                 }
+ 
+                 if (not(is.na(owner))) {
+                   data <- paste(data,
+                                 paste0("filter(", owner, ")"),
+                                 sep = " %>% \n    ")
+                 }
+ 
+                 ## Combine
+                 model_formula_local <- model_formula %>%
+                   gsub("\\[\\_yvar\\_\\]", outcome, .) %>%
+                   gsub("\\[\\_xvars\\_\\]",
+                        xvars,
+                        .) %>%
+                   gsub("\\[\\_data\\_\\]",
+                        data,
+                        .) %>%
+                   gsub("\\[\\_subset\\_\\]",
+                        ifelse(is.na(ltr), "NULL", ltr),
+                        .)
+                 
+                 ## Return
+                 model_formula_list_main[[counter]] <-
+                   list(
+                     outcome = outcome,
+                     predictor = predictor,
+                     moderator = moderator,
+                     regional = gsub("\\[\\_pred\\_\\]",
+                                     predictor,
+                                     regional),
+                     owner = owner,
+                     subset = ltr,
+                     rent_var = rent_var,
+                     controls = controls,
+                     formula = model_formula_local
+                   )
+               }
+             }
+           }
+         }
+       }
+     }
+   }
+ }

> ## ---- Estimation (main analyses) ----
> dir.create("est/stayer_analyses_infladj")

> focal_analyses <-
+   c(
+     209,
+     224
+   )

> ## Capture start time
> analysis <- 1

> ptm <- proc.time()[3]

> #for (analysis in analysis:length(model_formula_list_main)) {
> for (analysis in focal_analyses) {
+   ## Extract information for prediction
+   mfx_vars <- 
+     na.omit(unlist(model_formula_list_main[[analysis]][c("predictor", "rent_var")]))
+   mfx_vars <- paste(rep(mfx_vars, each = 2L), c("cwu", "umn"), sep = "_")
+   mod_var <- na.omit(model_formula_list_main[[analysis]]$moderator)
+   reg_var <- na.omit(model_formula_list_main[[analysis]]$regional)
+   
+   ## Estimate model in parallel
+   current_formula <- model_formula_list_main[[analysis]]$formula
+   
+   ## Set up cluster for parallelized computation across imputations
+   cl <- makeCluster(length(soep_imp$imputations),
+                     outfile = "est/stayer_analyses_infladj_main.txt",
+                     type = "FORK")
+   
+   ## Model estimation
+   est <- parLapply(cl,
+                    seq_along(soep_imp$imputations),
+                    function (m) {
+                      eval(parse(text = current_formula))
+                    })
+   
+   ## Exit parallel computation
+   stopCluster(cl)
+   
+   ## Model data
+   dat <- est[[1]]@frame %>%
+     droplevels()
+   
+   ## Descriptives
+   get_descriptives(
+     dat,
+     mfx_vars = mfx_vars,
+     mod_var = mod_var,
+     reg_var = reg_var,
+     num_breaks = 31L
+   ) %>%
+     write.csv(paste0(
+       "est/stayer_analyses_infladj/",
+       "descriptives",
+       analysis,
+       "_main.csv"
+     ))
+   
+   ## General and variables-specific prediction values
+   xmeans <- bind_cols(
+     dat %>%
+       summarize_if(is.numeric, median, na.rm = TRUE),
+     dat %>%
+       mutate_if(is.character, as.factor) %>%
+       summarize_if(is.factor, get_mode)
+   )
+   if ("log_hinc_eq" %in% names(dat))
+     log_hinc_eq <- seq(
+       quantile(dat$log_hinc_eq, .0005, na.rm = TRUE),
+       quantile(dat$log_hinc_eq, .9995, na.rm = TRUE),
+       length.out = 21L
+     )
+   if ("log_hinc_eq_cat" %in% names(dat))
+     log_hinc_eq_cat <- levels(dat$log_hinc_eq_cat)
+   if ("gtyp3" %in% names(dat))
+     gtyp3 <- levels(as.factor(dat$gtyp3))
+   if ("rent_cat" %in% names(dat))
+     rent_cat <- levels(dat$rent_cat)
+   if ("rent_chg_cat" %in% names(dat))
+     rent_chg_cat <- levels(dat$rent_chg_cat)
+   if ("cmr_arm_cat" %in% names(dat))
+     cmr_arm_cat <- levels(dat$cmr_arm_cat)
+   if ("cmr_arm_chg_cat" %in% names(dat))
+     cmr_arm_chg_cat <- levels(dat$cmr_arm_chg_cat)
+   
+   ## Prediction data
+   if (length(mod_var) == 0 & length(reg_var) == 0) {
+     pred_data <- xmeans
+     at_values <- NULL
+   } else if (length(mod_var) > 0 & length(reg_var) == 0) {
+     pred_data <- crossing(!!as.name(mod_var),
+                           xmeans,
+                           .name_repair = ~ make.names(., unique = TRUE))
+     at_values <- list(get(eval(mod_var)))
+     names(at_values) <- mod_var
+   } else if (length(mod_var) == 0 & length(reg_var) > 0) {
+     pred_data <- crossing(
+       !!as.name(reg_var),
+       xmeans,
+       .name_repair = ~ make.names(., unique = TRUE)
+     )
+     at_values <- list(get(eval(reg_var)))
+     names(at_values) <- reg_var
+   } else if (length(mod_var) > 0 & length(reg_var) > 0) {
+     pred_data <- crossing(!!as.name(mod_var),
+                           !!as.name(reg_var),
+                           xmeans,
+                           .name_repair = ~ make.names(., unique = TRUE)
+     )
+     at_values <- list(get(eval(mod_var)),
+                       get(eval(reg_var)))
+     names(at_values) <- c(mod_var, reg_var)
+   }
+   
+   ## Set up cluster for parallelized computation across imputations
+   cl <- makeCluster(length(soep_imp$imputations),
+                     outfile = "est/stayer_analyses_infladj.txt",
+                     type = "FORK")
+   
+   ## Marginal effects
+   mfx <- parLapply(cl,
+                    seq_along(est),
+                    function (m) {
+                      margins(
+                        est[[m]],
+                        data = pred_data,
+                        variables = mfx_vars,
+                        at = at_values,
+                        vce = "delta",
+                        allow.new.levels = TRUE
+                      ) %>%
+                        summary()
+                    })
+   
+   ## Exit parallel computation
+   stopCluster(cl)
+   
+   ## Export output
+   # Model matrix (stacked)
+   mod_summary <- lapply(est, summary)
+   
+   # Estimates
+   data.frame(
+     model_id = analysis,
+     outcome = model_formula_list_main[[analysis]]$outcome,
+     predictor = model_formula_list_main[[analysis]]$predictor,
+     moderator = model_formula_list_main[[analysis]]$moderator,
+     regional =  model_formula_list_main[[analysis]]$regional,
+     owner = model_formula_list_main[[analysis]]$owner,
+     spec = model_formula_list_main[[analysis]]$subset,
+     rent_var = model_formula_list_main[[analysis]]$rent_var,
+     controls = model_formula_list_main[[analysis]]$controls,
+     N_obs = nrow(est[[1]]@pp$X),
+     N_id = nlevels(est[[1]]@flist$id),
+     N_plz = nlevels(est[[1]]@flist$plz),
+     N_kkz = nlevels(est[[1]]@flist$kkz),
+     convergence_imp1 = est[[1]]@optinfo$conv$opt,
+     convergence_imp2 = est[[2]]@optinfo$conv$opt,
+     convergence_imp3 = est[[3]]@optinfo$conv$opt,
+     convergence_imp4 = est[[4]]@optinfo$conv$opt,
+     convergence_imp5 = est[[5]]@optinfo$conv$opt,
+     sigma_res_imp1 = mod_summary[[1]]$sigma,
+     sigma_id_imp1  = attr(mod_summary[[1]]$varcor$id, "stddev"),
+     sigma_plz_imp1 = attr(mod_summary[[1]]$varcor$plz, "stddev"),
+     sigma_kkz_imp1 = attr(mod_summary[[1]]$varcor$kkz, "stddev"),
+     sigma_res_imp2 = mod_summary[[2]]$sigma,
+     sigma_id_imp2  = attr(mod_summary[[2]]$varcor$id, "stddev"),
+     sigma_plz_imp2 = attr(mod_summary[[2]]$varcor$plz, "stddev"),
+     sigma_kkz_imp2 = attr(mod_summary[[2]]$varcor$kkz, "stddev"),
+     sigma_res_imp3 = mod_summary[[3]]$sigma,
+     sigma_id_imp3  = attr(mod_summary[[3]]$varcor$id, "stddev"),
+     sigma_plz_imp3 = attr(mod_summary[[3]]$varcor$plz, "stddev"),
+     sigma_kkz_imp3 = attr(mod_summary[[3]]$varcor$kkz, "stddev"),
+     sigma_res_imp4 = mod_summary[[4]]$sigma,
+     sigma_id_imp4  = attr(mod_summary[[4]]$varcor$id, "stddev"),
+     sigma_plz_imp4 = attr(mod_summary[[4]]$varcor$plz, "stddev"),
+     sigma_kkz_imp4 = attr(mod_summary[[4]]$varcor$kkz, "stddev"),
+     sigma_res_imp5 = mod_summary[[5]]$sigma,
+     sigma_id_imp5  = attr(mod_summary[[5]]$varcor$id, "stddev"),
+     sigma_plz_imp5 = attr(mod_summary[[5]]$varcor$plz, "stddev"),
+     sigma_kkz_imp5 = attr(mod_summary[[5]]$varcor$kkz, "stddev")
+   ) %>%
+     write.csv(paste0(
+       "est/stayer_analyses_infladj/",
+       "model_info",
+       analysis,
+       "_main.csv"
+     ))
+   
+   model_coefficients <- cbind(
+     data.frame(
+       model_id = analysis,
+       x_names = names(fixef(est[[1]])),
+       b_imp1 = fixef(est[[1]]),
+       b_imp2 = fixef(est[[2]]),
+       b_imp3 = fixef(est[[3]]),
+       b_imp4 = fixef(est[[4]]),
+       b_imp5 = fixef(est[[5]])
+     ),
+     as.data.frame(as.matrix(vcov(est[[1]]))) %>%
+       rename_all(function (x)
+         paste0(x, "_imp1")),
+     as.data.frame(as.matrix(vcov(est[[2]]))) %>%
+       rename_all(function (x)
+         paste0(x, "_imp2")),
+     as.data.frame(as.matrix(vcov(est[[3]]))) %>%
+       rename_all(function (x)
+         paste0(x, "_imp3")),
+     as.data.frame(as.matrix(vcov(est[[4]]))) %>%
+       rename_all(function (x)
+         paste0(x, "_imp4")),
+     as.data.frame(as.matrix(vcov(est[[5]]))) %>%
+       rename_all(function (x)
+         paste0(x, "_imp5"))
+   ) %>%
+     write.csv(paste0(
+       "est/stayer_analyses_infladj/",
+       "model_coefficients",
+       analysis,
+       "_main.csv"
+     ))
+   
+   pool_mfx_summary(mfx) %>%
+     as_tibble() %>%
+     mutate(model_id = analysis) %>%
+     relocate(model_id, .before = factor) %>%
+     write.csv(paste0(
+       "est/stayer_analyses_infladj/",
+       "marginal_effects",
+       analysis,
+       "_main.csv"
+     ))
+   
+   ## Clear memory
+   rm(est, mod_summary, mfx, dat)
+   gc(reset = TRUE)
+   
+   ## Progress
+   print(paste0(
+     "Analysis: ",
+     analysis,
+     ". Time elapsed: ",
+     round((proc.time()[3] - ptm) / 60, 2),
+     " minutes."
+   ))
+ }
[1] "Analysis: 209. Time elapsed: 2.67 minutes."
[1] "Analysis: 224. Time elapsed: 3.95 minutes."

> ## ---- Collect output ----
> model_files <- list.files("est/stayer_analyses_infladj")

> model_info_files_main <-
+   model_files[startsWith(model_files, "model_info") &
+                 endsWith(model_files, "_main.csv")]

> model_coefficients_files_main <-
+   model_files[startsWith(model_files, "model_coefficients") &
+                 endsWith(model_files, "_main.csv")]

> marginal_effects_files_main <-
+   model_files[startsWith(model_files, "marginal_effects") &
+                 endsWith(model_files, "_main.csv")]

> descriptives_files_main <-
+   model_files[startsWith(model_files, "descriptives") &
+                 endsWith(model_files, "_main.csv")]

> ## ---- Main analyses ----
> ## Model info
> model_info <- data.frame()

> for (file in model_info_files_main) {
+   model_info <- model_info %>%
+     bind_rows(read.csv(paste0("est/stayer_analyses_infladj/",
+                               file)))
+ }

> model_info <- model_info %>%
+   dplyr::select(-X) %>%
+   arrange(model_id)

> model_info %>%
+   write.csv("est/stayer_analyses_infladj_model_info_main.csv")

> ## Model coefficients
> model_coefficients <- data.frame()

> for (file in model_coefficients_files_main) {
+   model_coefficients <- model_coefficients %>%
+     bind_rows(read.csv(paste0("est/stayer_analyses_infladj/",
+                               file)))
+ }

> model_coefficients <- model_coefficients %>%
+   dplyr::select(-X) %>%
+   arrange(model_id)

> model_coefficients %>%
+   write.csv("est/stayer_analyses_infladj_model_coefficients_main.csv")

> ## Marginal effects
> marginal_effects <- data.frame()

> for (file in marginal_effects_files_main) {
+   marginal_effects <- marginal_effects %>%
+     bind_rows(read.csv(paste0("est/stayer_analyses_infladj/",
+                               file)))
+ }

> marginal_effects <- marginal_effects %>%
+   dplyr::select(-X) %>%
+   arrange(model_id)

> marginal_effects %>%
+   write.csv("est/stayer_analyses_infladj_marginal_effects_main.csv")

> ## Descriptives
> descriptives <- data.frame()

> for (file in descriptives_files_main) {
+   descriptives <- descriptives %>%
+     bind_rows(read.csv(paste0("est/stayer_analyses_infladj/",
+                               file)) %>%
+                 mutate(model_id = readr::parse_number(file)))
+ }

> descriptives <- descriptives %>%
+   dplyr::select(-X) %>%
+   arrange(model_id)

> descriptives %>%
+   write.csv("est/stayer_analyses_infladj_descriptives_main.csv")
